Sex/Gender Modifies the Association Between the <i>MC4R</i> p.Ile269Asn Mutation and Type 2 Diabetes in the Mexican Population
Bibliographic record
Abstract
CONTEXT: Studies in mice and humans suggest that melanocortin-4 receptor (MC4R) deficiency affects body weight in a sex-/gender-dependent manner. However, similar evidence for type 2 diabetes (T2D) is scarce. OBJECTIVE AND DESIGN: We investigated whether sex/gender modifies the association between the loss-of-function MC4R p.Ile269Asn mutation and T2D in 6929 Mexican adults (3175 T2D cases and 3754 normal glucose tolerance [NGT] controls). The 2003 American Diabetes Association criteria were used to define NGT and T2D. The MC4R p.Ile269Asn mutation was genotyped in all participants using TaqMan technology. RESULTS: The MC4R p.Ile269Asn mutation was associated with T2D in 6929 Mexican adults (Ncontrols = 3754, Ncases = 3175, odds ratio [OR] = 2.00, 95% confidence interval [CI], 1.35-2.97; P = 5.7 × 10-4). The MC4R p.Ile269Asn mutation had a frequency of 0.86 and 1.05% in women with NGT and T2D, and 0.78 and 1.32% in men with NGT and T2D, respectively. We identified a significant interaction between the MC4R p.Ile269Asn mutation and sex/gender on T2D risk (P = 0.049). Although a strong association between the mutation and T2D was observed in men (Ncontrols = 2418, Ncases = 1807, OR = 2.63, 95% CI, 1.62-4.28, P = 9.3 × 10-5), results were not significant in women (Ncontrols = 1336, Ncases = 1368, OR = 1.16, 95% CI, 0.60-2.26, P = 0.65). Further adjustment for body mass index in the logistic regression model did not alter the sex-/gender-specific pattern of association (men: OR = 2.22, 95% CI, 1.34-3.67, P = 0.0019; women: OR = 1.02, 95% CI, 0.51-2.02, P = 0.95). CONCLUSION: This is the first report of a male-specific association between the MC4R p.Ile269Asn loss-of-function mutation and T2D in the Mexican population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".